Projet SPICES : sélection parmi sept recommandations de bonne pratique pour la prévention des maladies cardiovasculaires via les procédures Agree II et ADAPTE. Identification des références bibliographiques des recommandations internationales <i>National Stroke Foundation, Guidelines for the management of absolute cardiovascular disease risk, 2012</i> et <i>Canadian Diabetes Association, Clinical practice guidelines for the prevention and management of diabetes in Canada, 2013</i>, à intégrer dans la matrice de recherche de l’étude SPICES
Bibliographic record
Abstract
Cardiovascular diseases (CVD) are the first cause of death worldwide. CVD risk factors are known and CVD primary prevention is possible. However, current main strategies are focused on medication and medical access is not sustainable, especially in low-income countries. Changes are necessary. SPICES aim was to implement non-pharmacologic CVD primary prevention recommendations in communities. A systematic literature review seeking to implementation was conducted. The ADAPTE process and its AGREE 2 tool allowed selection of efficient prevention interventions among the best international guidelines.Method : seven guidelines were analyzed with the AGREE 2 tool. Guidelines with a double overall assessment equal or superior to 5 were selected. Then, recommendations concerning CVD non-pharmacological primary prevention for communities with an A or B level of evidence were extracted from two retained guidelines. References supporting recommendations were extracted and gathered in a matrix if they were of high level of evidence (systematic review, randomized controlled trial, cohort study) and had a positive outcome.Results : among the seven guidelines, six were retained. 53 references were selected among 22 recommendations.Discussion : an exhaustive CVD primary prevention international guidelines review and the extraction of their references were accomplished for SPICES. The choice of ADAPTE was creative. ADAPTE differed from PRISMA because of its implementation focus. Information bias and selection bias were limited by consensus between SPICES project members and by selection of high level of evidence guidelines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.125 | 0.337 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.026 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".